Reinforcement learning processes as forecasters of depression remission.

ABSTRACT:

Background

Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness.

Methods

We applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up

SUBMITTER: Bansal V 

PROVIDER: S-EPMC11573115 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature